# Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction

arXiv: https://arxiv.org/abs/2402.14521 · HTML: https://mlsanity.com/paper/2402.14521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://mlsanity.com/paper/1906.04684/) (2019)
- [Global-to-Local Neural Networks for Document-Level Relation Extraction](https://mlsanity.com/paper/2009.10359/) (2020)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement](https://mlsanity.com/paper/2301.10684/) (2023)
- [FiNER-ORD: Financial Named Entity Recognition Open Research Dataset](https://mlsanity.com/paper/2302.11157/) (2023)
- [NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages](https://mlsanity.com/paper/2105.00810/) (2021)

## Abstract (arXiv)

Standard English and Malaysian English exhibit notable differences, posing challenges for natural language processing (NLP) tasks on Malaysian English. Unfortunately, most of the existing datasets are mainly based on standard English and therefore inadequate for improving NLP tasks in Malaysian English. An experiment using state-of-the-art Named Entity Recognition (NER) solutions on Malaysian English news articles highlights that they cannot handle morphosyntactic variations in Malaysian English. To the best of our knowledge, there is no annotated dataset available to improvise the model. To address these issues, we constructed a Malaysian English News (MEN) dataset, which contains 200 news articles that are manually annotated with entities and relations. We then fine-tuned the spaCy NER tool and validated that having a dataset tailor-made for Malaysian English could improve the performance of NER in Malaysian English significantly. This paper presents our effort in the data acquisition, annotation methodology, and thorough analysis of the annotated dataset. To validate the quality of the annotation, inter-annotator agreement was used, followed by adjudication of disagreements by a subject matter expert. Upon completion of these tasks, we managed to develop a dataset with 6,061 entities and 3,268 relation instances. Finally, we discuss on spaCy fine-tuning setup and analysis on the NER performance. This unique dataset will contribute significantly to the advancement of NLP research in Malaysian English, allowing researchers to accelerate their progress, particularly in NER and relation extraction. The dataset and annotation guideline has been published on Github.